Origin, weathering and paleoclimatic significance of Middle - late Pleistocene slope covers, Mt. Kenya, Kenya
Bibliographic record
Abstract
Stacks of the Pleistocene tills and associated airfall/slopewash/colluvial sediment abound on East African Mountains but few localities exist where thick deposits of middle to Late Pleistocene age can be studied to bedrock with topography the main soil-forming agent over <0.8 Ma. Two tills form the main structure of the catena, the oldest buried in the crest, backslope and footslope of the deposit, the youngest forming the crest and upper backslope, with massive colluvial infill forming a still younger sediment mass superposed on older sediment in the lower backslope, footslope and toeslope, the latter all radiocarbon dated to within the last glaciation (Liki on Mt. Kenya; Weichselian in Europe, Wisconsin in North America). The moraine stack, first identified by J.W. Gregory in the late 19th century, as belonging to the ‘Older Glaciation’ (Illinoian in North America; Teleki on Mt. Kenya), is much older than originally thought with tills and other paraglacial sediment extending to saprolitic bedrock, paleomagnetic assessment and relative weathering indices placing the mass in the Brunhes Chron. These results demonstrate that despite erosion and weathering, paleosols in toposequences near the margins of successive glaciations retain properties allowing reconstruction of environmental changes over long periods of time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".